This can be seen as a generalisation of PCAPrincipal component analysis reduces the dimensions of the input variables by taking only the directions of the largest eigenvalues. to the case where the output variable
is taken into account ([1]} and [2]} develop
the idea). Standard supervised PCA assumes that the output is
distributed as a gaussian distribution, which is a dangerous simplification
in the classification setting[3]}.
